Microphone arrays are extensively applied in intelligent speech communication systems. Multichannel speech enhancement, owing to its additional spatial information, is widely studied in recent years and tends to have superior performance relative to monaural speech enhancement. Beamforming [1]} is a classic multichannel speech enhancement model preserving the desired signal from the target direction and suppressing signals from other directions. Different in the manners of utilizing spatial information, recent studies on multichannel speech enhancement DNNs can be divided into two categories: i) monaural deep masking or mapping [2]} followed by beamforming [3]},[4]}; ii) directly learning spatial information by inputting multichannel signals or cross-channel features into the network, and performing end-to-end speech enhancement. Methods in the first class have become the most popular front-ends for automatic speech recognition (ASR) [5]}. In addition, they are free from the microphone array generalization problem, since both monaural speech enhancement DNN and beamforming can be flexibly applied on any array configurations. Especially, [6]} and [7]} further strengthen the array generalization capability by exhaustively employing each microphone in an array as a reference microphone. However, these approaches solely focus on learning monaural speech T-F information channel by channel with DNNs and the performance strongly depends on the estimation accuracy of beamforming parameters. We thus believe that simultaneously exploiting the spatial and monaural information with DNN could further improve the estimation of speech on target direction, as is done in the end-to-end time-domain methods [8]}, [9]} and T-F domain methods [10]}, [11]}. Neural beamforming takes the multichannel signals as input and explicitly performs as a beamformer [12]}, [13]}, [14]} and sometimes it is trained to leverage the spatial cues, such as interaural phase/level difference in [15]}, [16]}, [17]}. These methods apparently have the microphone array generalization problem which, however, have not been considered in their original papers. Very recently, the FaSNet [18]} integrated with a transform-average-concatenate (TAC) [19]} was proposed to process signals with varying number of microphones, where the TAC module together with cross-correlation functions between channels were employed to learn the spatial information. In [20]}, a self-attention network is used to exploit the spatial information with a varying number of microphones. Both these two methods, i.e. [19]} and [20]}, alternate the temporal processing layers and spatial processing layers, which is arguably problematic for learning some temporal-spatial-combined information, such as the directional continuity of speakers and the spatial diffuseness of noise.
<FIGURE>